{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "23fedda6-3299-43e8-8166-2b6066bfa56f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7da05eb9-57a7-47cf-a854-17b911c5b8eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#中文设置\n",
    "plt.rcParams['font.sans-serif']=[\"SimHei\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "52a36aaf-9966-4423-a572-2906e379f426",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
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       "      <th>SibSp</th>\n",
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       "      <th>Embarked</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
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       "      <td>PC 17599</td>\n",
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       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
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       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df =pd.read_csv('train.csv' )\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e6f2e286-4d09-41ed-87d3-3a895de75515",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 熟悉数据\n",
    "# ·PassengerId: 乘客ID\n",
    "# ·Survived:是否获救，1表示获救，0表示没有获救\n",
    "# ·Pclass:乘客等级，“1”表示Upper,“2”表示Middle,“3”表示Lower\n",
    "# ·Name: 乘客姓名\n",
    "# ·Sex:性别\n",
    "# ·Age:年龄\n",
    "# ·SibSp: 乘客在船上的配偶数量或兄弟姐妹数量\n",
    "# ·Parch:乘客在船上的父母或子女数量\n",
    "# ·Ticket:船票信息\n",
    "# ·Fare: 票价\n",
    "# ·Cabin: 是否住在独立的房间，“1”表示是，“0”为否\n",
    "# ·embarked:表示乘客上船的码头距离泰坦尼克出发码头的距离，数值越大表示距离越远"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "224e7d4d-d066-43d8-a841-2ca9849ca581",
   "metadata": {},
   "source": [
    "1.查看各数据类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "93999cdd-d14e-4146-ab1b-505c3c1f8f97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48396d51-22f0-4ea5-be74-7d0edb2fd43a",
   "metadata": {},
   "source": [
    "2.查看数据的摘要信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2a19a93a-d6f7-4487-a402-f59a709c6a0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "613e612b-04ea-410b-9ff0-a965760ad706",
   "metadata": {},
   "source": [
    "3.数据清洗和处理缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e4340fb9-4855-420c-8431-568072a8bc82",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Embarked']=df['Embarked'].fillna('S') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0125305d-95bd-43aa-95e3-4a083ae79afb",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex        Age  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.000000   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.000000   \n",
       "2                               Heikkinen, Miss. Laina  female  26.000000   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.000000   \n",
       "4                             Allen, Mr. William Henry    male  35.000000   \n",
       "..                                                 ...     ...        ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.000000   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.000000   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female  29.699118   \n",
       "889                              Behr, Mr. Karl Howell    male  26.000000   \n",
       "890                                Dooley, Mr. Patrick    male  32.000000   \n",
       "\n",
       "     SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "0        1      0         A/5 21171   7.2500   NaN        S  \n",
       "1        1      0          PC 17599  71.2833   C85        C  \n",
       "2        0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        1      0            113803  53.1000  C123        S  \n",
       "4        0      0            373450   8.0500   NaN        S  \n",
       "..     ...    ...               ...      ...   ...      ...  \n",
       "886      0      0            211536  13.0000   NaN        S  \n",
       "887      0      0            112053  30.0000   B42        S  \n",
       "888      1      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0      0            111369  30.0000  C148        C  \n",
       "890      0      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#处理Age的缺失值，Age 是连续数据，这里用平均值填充缺失值\n",
    "age_mean = df['Age'].mean()\n",
    "df['Age'] = df['Age'].fillna(age_mean)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe365a56-06ec-4609-be4c-cd15d2e414d7",
   "metadata": {},
   "source": [
    "4.处理性别数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "46e47dbf-c084-43dd-8056-f463d061469e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sex_value(Sex):\n",
    "    if Sex=='male':\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "04a0c0c7-f78b-41d9-8f86-1304364ecd88",
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       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
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       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
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     "execution_count": 16,
     "metadata": {},
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   ],
   "source": [
    "df['Sex'].apply(sex_value)\n",
    "df.head()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 17,
   "id": "b93e4c3e-9b51-4743-96a4-cbacecfa4147",
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       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name  Sex   Age  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    1  22.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0  38.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina    0  26.0      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    0  35.0      1      0   \n",
       "4                           Allen, Mr. William Henry    1  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin Embarked  \n",
       "0         A/5 21171   7.2500   NaN        S  \n",
       "1          PC 17599  71.2833   C85        C  \n",
       "2  STON/O2. 3101282   7.9250   NaN        S  \n",
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     "execution_count": 17,
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   ],
   "source": [
    "# 使 用map方法进行映射处理，如果没有匹配，返回的是NaN\n",
    "df[ 'Sex' ]=df['Sex' ].map({\"male\" :1,\"female\"    :0})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "979af976-adf9-40a7-bac0-4c62dfde4f76",
   "metadata": {},
   "source": [
    "5.获取生还乘客的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c61a2825-d513-4375-855b-d0651e7b8298",
   "metadata": {},
   "outputs": [
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>876</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Najib, Miss. Adele Kiamie \"Jane\"</td>\n",
       "      <td>0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2667</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>880</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</td>\n",
       "      <td>0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11767</td>\n",
       "      <td>83.1583</td>\n",
       "      <td>C50</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>881</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Shelley, Mrs. William (Imanita Parrish Hall)</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>230433</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>342 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "8              9         1       3   \n",
       "9             10         1       2   \n",
       "..           ...       ...     ...   \n",
       "875          876         1       3   \n",
       "879          880         1       1   \n",
       "880          881         1       2   \n",
       "887          888         1       1   \n",
       "889          890         1       1   \n",
       "\n",
       "                                                  Name  Sex   Age  SibSp  \\\n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...    0  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina    0  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)    0  35.0      1   \n",
       "8    Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)    0  27.0      0   \n",
       "9                  Nasser, Mrs. Nicholas (Adele Achem)    0  14.0      1   \n",
       "..                                                 ...  ...   ...    ...   \n",
       "875                   Najib, Miss. Adele Kiamie \"Jane\"    0  15.0      0   \n",
       "879      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)    0  56.0      0   \n",
       "880       Shelley, Mrs. William (Imanita Parrish Hall)    0  25.0      0   \n",
       "887                       Graham, Miss. Margaret Edith    0  19.0      0   \n",
       "889                              Behr, Mr. Karl Howell    1  26.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
       "1        0          PC 17599  71.2833   C85        C  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "8        2            347742  11.1333   NaN        S  \n",
       "9        0            237736  30.0708   NaN        C  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "875      0              2667   7.2250   NaN        C  \n",
       "879      1             11767  83.1583   C50        C  \n",
       "880      1            230433  26.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "\n",
       "[342 rows x 12 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survives_passenger_df = df[df['Survived']==1]\n",
    "survives_passenger_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05e8b755-2fcc-412c-9eab-31f19b14dd69",
   "metadata": {},
   "source": [
    "6.性别对生还率的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6496348b-5857-413d-b65f-ec2a7f34e7d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      0\n",
       "2      0\n",
       "3      0\n",
       "8      0\n",
       "9      0\n",
       "      ..\n",
       "875    0\n",
       "879    0\n",
       "880    0\n",
       "887    0\n",
       "889    1\n",
       "Name: Sex, Length: 342, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#获取生还者的性别信息\n",
    "df_sex1 = df['Sex'][df['Survived']==1]\n",
    "df_sex1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0ee2753f-a603-4af2-bb76-d9f482a0614c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "4      1\n",
       "5      1\n",
       "6      1\n",
       "7      1\n",
       "      ..\n",
       "884    1\n",
       "885    0\n",
       "886    1\n",
       "888    0\n",
       "890    1\n",
       "Name: Sex, Length: 549, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#获取遇难者性别数据\n",
    "df_sex0=df['Sex'][df['Survived']==0] \n",
    "df_sex0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d86f3101-2085-42a6-a26b-93aff88ec0ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([233.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0., 109.]),\n",
       " array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#生还者直方图\n",
    "plt.hist(df_sex1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "eda34d2a-61ab-477b-9f61-f65912ae0f7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 81.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0., 468.]),\n",
       " array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_sex0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b7d5d8f3-87c9-44bc-b999-91caca451a5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x1eb86f482c0>,\n",
       "  <matplotlib.axis.XTick at 0x1eb86f48290>,\n",
       "  <matplotlib.axis.XTick at 0x1eb86f33d70>,\n",
       "  <matplotlib.axis.XTick at 0x1eb86f86030>],\n",
       " [Text(-1, 0, '-1'), Text(0, 0, 'F'), Text(1, 0, 'M'), Text(2, 0, '2')])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制直方图\n",
    "plt.hist([df_sex1,df_sex0],\n",
    "        stacked=True,\n",
    "        label=['Rescued','not saved'])\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.title('Sex_survived')\n",
    "\n",
    "plt.xticks([-1,0,1,2],[-1,'F','M',2])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7da2963-2b10-4b54-ad2e-2aa6d802df05",
   "metadata": {},
   "source": [
    "7.乘客等级对生还率的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "99cf691c-223b-4a62-a5cb-b818d9dc46a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Pclass Survived')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#不同等级对生还率的影响\n",
    "df_Pclass1 = df['Pclass'][df['Survived']==1]\n",
    "df_Pclass0 = df['Pclass'][df['Survived']==0]\n",
    "\n",
    "plt.hist([df_Pclass1,df_Pclass0],\n",
    "        stacked=True,\n",
    "        label=['Rescued','not saved'])\n",
    "\n",
    "plt.xticks([1,2,3],['Upper','Middle','lower'])\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.title('Pclass Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c70976a9-d28b-400d-8c71-a08c83abae52",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Upper等级生还概率大于Middle、lower的生存概率，等级越好生还概率越好"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04758164-57ce-429d-9f01-f7860928b4f2",
   "metadata": {},
   "source": [
    "8. 年 龄 对 生 还 率 的 影 响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0083a4d2-d362-49d5-b8f1-571604f8802d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Age_Survived')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_Age1 = df['Age'][df['Survived']==1]\n",
    "df_Age0 = df['Age'][df['Survived']==0]\n",
    "\n",
    "plt.hist([df_Age1,df_Age0],\n",
    "stacked=True,\n",
    "label=['Rescued','not saved'])\n",
    "\n",
    "plt.legend() \n",
    "\n",
    "plt.title('title')\n",
    "\n",
    "plt.title('Age_Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ff03d7c2-f80c-4a4e-8175-5a34ae4af762",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2\n",
       "1      2\n",
       "2      2\n",
       "3      2\n",
       "4      2\n",
       "      ..\n",
       "886    2\n",
       "887    2\n",
       "888    2\n",
       "889    2\n",
       "890    2\n",
       "Name: Age, Length: 891, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#不同年龄段对生还率的影响elderly,child,youth\n",
    "#年龄数据进行处理，Θ-18为child(少年),18-40为youth(青年),40-80为elderly(老年)\n",
    "def age_duan(age):\n",
    "    if age<=18:\n",
    "        return 1\n",
    "    elif age<=40:\n",
    "        return 2\n",
    "    else:\n",
    "        return 3\n",
    "            \n",
    "df['Age']=df['Age'].apply(age_duan)\n",
    "df['Age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "cc0a9a56-7f0c-4738-8bf4-0686ef443dc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Age_Survived')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_Age1=df[ 'Age' ][df[   'Survived'   ]==1]\n",
    "df_Age0=df[ 'Age' ][df[   'Survived'   ]==0]\n",
    "\n",
    "plt.hist([df_Age1,df_Age0],\n",
    "stacked= True ,\n",
    "label=['Rescued','not saved'])\n",
    "\n",
    "plt.xticks([1,2,3],['child','youth','elderly'])\n",
    "plt.legend()\n",
    "plt.title('Age_Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4066c67d-ffc9-4da1-b0cf-c8b023cd00aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "#0-10岁的生还率率最高，20-40之间的生还人数最多"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12e9b6a0-3a3f-4d53-9746-3ffb98c6e99e",
   "metadata": {},
   "source": [
    "9.性别和乘客等级共同对生还率的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c7b08577-1051-40b3-bba6-f48099c025ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0</th>\n",
       "      <th>1</th>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>81</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>10</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>6</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">1</th>\n",
       "      <th>1</th>\n",
       "      <td>122</td>\n",
       "      <td>122</td>\n",
       "      <td>122</td>\n",
       "      <td>122</td>\n",
       "      <td>122</td>\n",
       "      <td>122</td>\n",
       "      <td>122</td>\n",
       "      <td>122</td>\n",
       "      <td>95</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
       "      <td>6</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>347</td>\n",
       "      <td>347</td>\n",
       "      <td>347</td>\n",
       "      <td>347</td>\n",
       "      <td>347</td>\n",
       "      <td>347</td>\n",
       "      <td>347</td>\n",
       "      <td>347</td>\n",
       "      <td>6</td>\n",
       "      <td>347</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            PassengerId  Survived  Name  Age  SibSp  Parch  Ticket  Fare  \\\n",
       "Sex Pclass                                                                 \n",
       "0   1                94        94    94   94     94     94      94    94   \n",
       "    2                76        76    76   76     76     76      76    76   \n",
       "    3               144       144   144  144    144    144     144   144   \n",
       "1   1               122       122   122  122    122    122     122   122   \n",
       "    2               108       108   108  108    108    108     108   108   \n",
       "    3               347       347   347  347    347    347     347   347   \n",
       "\n",
       "            Cabin  Embarked  \n",
       "Sex Pclass                   \n",
       "0   1          81        94  \n",
       "    2          10        76  \n",
       "    3           6       144  \n",
       "1   1          95       122  \n",
       "    2           6       108  \n",
       "    3           6       347  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Sex','Pclass']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "011f3070-69f4-45b5-86a4-b690c09e50be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex  Pclass\n",
       "0    1          94\n",
       "     2          76\n",
       "     3         144\n",
       "1    1         122\n",
       "     2         108\n",
       "     3         347\n",
       "Name: PassengerId, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#随便取出一列\n",
    "df.groupby(['Sex','Pclass'])['PassengerId'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "07e3c03e-7316-4768-9687-a8920259d282",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex  Pclass\n",
       "0    1          94\n",
       "     2          76\n",
       "     3         144\n",
       "1    1         122\n",
       "     2         108\n",
       "     3         347\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sex和Pclass分组后，每组中Sex 有多少个数据\n",
    "df.groupby(['Sex','Pclass'])['Sex'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9a3b8ba2-e744-42c6-93fa-bc9bc3ff6929",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex  Pclass\n",
       "0    1         0.968085\n",
       "     2         0.921053\n",
       "     3         0.500000\n",
       "1    1         0.368852\n",
       "     2         0.157407\n",
       "     3         0.135447\n",
       "Name: Sex, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#按照性别和乘客等级对乘客进行分组后，每个组的人数\n",
    "group_all =df.groupby(['Sex','Pclass'])['Sex'].count()\n",
    "\n",
    "#计算生还者中每组数据\n",
    "survives_passenger_df=df[df['Survived']==1]\n",
    "\n",
    "#对生还者进行统计操作\n",
    "survives_passenger_group = survives_passenger_df.groupby(['Sex','Pclass'])['Sex'].count()\n",
    "\n",
    "#每组生还者的比率\n",
    "survives_passenger_radio=survives_passenger_group/group_all\n",
    "survives_passenger_radio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "b197b2db-1487-45b5-a68c-6baf6a386e69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rectangle(xy=(-0.25, 0), width=0.5, height=0.968085, angle=0)\n",
      "Rectangle(xy=(0.75, 0), width=0.5, height=0.921053, angle=0)\n",
      "Rectangle(xy=(1.75, 0), width=0.5, height=0.5, angle=0)\n",
      "Rectangle(xy=(2.75, 0), width=0.5, height=0.368852, angle=0)\n",
      "Rectangle(xy=(3.75, 0), width=0.5, height=0.157407, angle=0)\n",
      "Rectangle(xy=(4.75, 0), width=0.5, height=0.135447, angle=0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.patches.Rectangle at 0x1eb82f6cef0>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.patches import Rectangle\n",
    "\n",
    "bar=survives_passenger_radio.plot.bar(title=\"性别和乘客等级共同对生还率的影响\")\n",
    "for p in bar.patches:\n",
    "    print (p)\n",
    "    bar.text(p.get_x()*1.005,p.get_height()*1.005,'%.2f%%'%(p.get_height()*100))\n",
    "\n",
    "Rectangle(xy=(-0.25,0),width=0.5,height=0.968085,angle=0)\n",
    "Rectangle(xy=(0.75,0),width=0.5,height=0.921053,angle=0)\n",
    "Rectangle(xy=(1.75,0),width=0.5,height=0.5,angle=0)\n",
    "Rectangle(xy=(2.75,0),width=0.5,height=0.368852,angle=0)\n",
    "Rectangle(xy=(3.75,0),width=0.5,height=0.157407,angle=0)\n",
    "Rectangle(xy=(4.75,0),width=0.5,height=0.135447,angle=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "54dce894-5d47-46b9-a704-14be3f0ef07d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#生还率的影响性别>乘客等级，其次是乘客等及对生还率的影响是1>2>3等"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ed3c129-edf3-4e53-a12e-0350be4c20b9",
   "metadata": {},
   "source": [
    "10.性别和年龄对生还率的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "d14f5477-656e-47a5-9728-4e1255631d60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#统计列生还人数和所有人数比率\n",
    "cols = ['Pclass','Sex','Age']\n",
    "\n",
    "def\tpassenger_survived_ratio(data,cols):\n",
    "    #所有乘客信息\n",
    "    passenger_group\t=data.groupby(cols)[cols[0]].count()\n",
    "\n",
    "    #生还者信息\n",
    "    surived_info=df[cols][df['Survived']==1]\n",
    "    surived_group=surived_info.groupby(cols)[cols[0]].count()\n",
    "    return surived_group/passenger_group\n",
    "\n",
    "def print_bar(data,title= \"\" ):\n",
    "    bar=data.plot.bar(title=title)\n",
    "    for p in bar.patches:\n",
    "        bar.text(p.get_x()*1.005,p.get_height()*1.005,'%.2f9%%'%(p.get_height()*100))\n",
    "\n",
    "print_bar(passenger_survived_ratio(df,['Age','Sex']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "32b9fabf-7608-4f28-a759-3193ed126f27",
   "metadata": {},
   "outputs": [],
   "source": [
    "#可以看出乘客的等级对生还率的影响>乘客年龄的影响 年龄越大生还率越小，乘客等级越差生还率越差"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1b3ca50-9296-4267-9737-8d555fc6d42b",
   "metadata": {},
   "source": [
    "结 论:\n",
    "通过分析，可以看出对生还率影响最大的因素是乘客等级，其次是性别，最后年龄段也对生还率有影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a78cd83-d10e-48f3-b269-e1e8f2224077",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
